Introduction

What is Flickr ? It is one of the largest photo management and sharing application in the world. It lets users share their photos on its platflorm and enable new ways of organizing photos and videos.

Project Proposal

• Identify regions where Flickr is used most and least.
• Flickr can exercise their marketing team where Flickr used least.
• Predict number of uploads based on tags used and region where the photo is uploaded.
• This Prediction matrix can be used to effectively design and strengthen the storage servers and the architecture of system to sustain traffic and manage the upcoming load.

Data Collection

• We are using below Flickr API’s to collect the data.

flickr.interestingness.getList flickr.tags.getListPhoto flickr.tags.getRelated flickr.tags.getHotList flickr.places.placesForTags

• We have got around 28000 instances of data after this step. Our target variable is photo count.

Original Plan

• We were predicting if a photo was uploaded with some tags in some region, how many photos with similar tags would get be uploaded soon.

• It has been seen that many tags have a correlation with the regions where the tag originates in.

• Prediction of the number of the uploads Flickr should expect are coming could help them manage their resources of storage and servers for the upcoming traffic and data.

• For this prediction we will be using the following prediction algorithms :

Knn Linear Regression Neural Network

Analytics

We have used Knn algorithm for predicting the photo count.

RMSE- 17511.46 MAE - 5843

Peer comments

Summary

• Sentiment Analysis of comments in a photo
• Locations where Flickr is most popular
• Popular tags in a country
• Most used camera brands and models used